System and method for auto-suggesting responses based on social conversational contents in customer care services

ABSTRACT

A first embodiment of the disclosure relates to a method for responding to a message posted in a social media stream. The method includes monitoring a social media site for at least one message including select subject matter. In response to identifying a message, the method includes collecting a series of exchanges that form a conversational thread including the message. The method includes determining at least one content attribute of the message. The method includes classifying the message using at least one key attribute. The method includes searching a database for a reference message using a combination of the at least one content and key attributes. The method includes determining a previous outcome of a reference thread including the reference message. The method includes using the previous outcome for determining a course of action.

BACKGROUND

The present disclosure relates to a method and system for automaticallysuggesting responses based on social conversational content in customercare services.

Smart phones and social media sites, such as, Twitter, Facebook,LinkedIn and Google Plus, etc., amplify customers' voice in themarketplace. Customers can research companies online, seekrecommendations through social media channels, and share opinions abouttheir experiences with certain companies, products and services. Theiractivity on these sites can affect a company's reputation. Becausesocial media is not as widely moderated as mainstream media, negativepostings by individuals can adversely impact others' perceptions of acompany. The impact on a brand or company can be far-reaching due to theviral spread of information via social communities connected online.Therefore, companies desire an access to these social conversations andparticipation in the dialogue as part of their social customerrelationship management (CRM) model.

CRM is used to manage a company's interactions with customers, clients,and sales prospects through social media channels. CRM uses technologyto organize, automate, and synchronize business processes—principallysales activities—marketing, customer service, and technical support. Theoverall goals are to find, attract, and earn new clients, nurture andretain existing clients, entice a return of former clients, and reducethe marketing and servicing costs to clients. CRM delivers customerbusiness data to help companies provide customers with desired productsand services, provide better customer service, cross-sell and up sellmore effectively, and close deals.

A typical CRM model monitors social media posts made about a selectproduct or service, select topics, customer sentiment, user profile, andinfluence. The posts are collected and forwarded to a customer carequery handling expert, which resolves any customer's issue by answeringqueries based on its knowledge and experience. Because social media is agrowing platform, dominated by conversational, transparent and instantinteractions among a network of connected customers, companies arechallenged to effectively provide customers with instant and accuratesolutions.

Therefore, the handling expert must be adept at identifying, analyzing,and solving the customers' problem. The conventional approach is amanual process requiring intensive expert training, so handling expertshave different degrees of knowledge. Some experts refer to documentationor senior agents before responding to queries, increasing the responsetime, which can lead to frustrated and dissatisfied customers.

In a traditional (telephonic) call center environment, service logsautomatically generate and/or recommend responses by categorizingverbal-to-textual content in the customer's conversation. However, theservice logs do not address social medial contexts presented in anonline environment, such as customer locations based on customerprofiles, geo-code check-ins, historical conversations with regards totopics, sentiments, and dialog sequence structure, etc. Furthermore, ascompared with traditional call-center systems, social contents are morenoisy, informal, and conversational in the CRM environment.

A desired CRM approach targets contextual information presented bysocial conversations to assist an agent's response.

INCORPORATION BY REFERENCE

The disclosure of co-pending and commonly assigned US Patent ApplicationPublication No. 2013/0080212, Published Mar. 28, 2013, entitled,“METHODS AND SYSTEMS FOR MEASURING ENGAGEMENT EFFECTIVENESS INELECTRONIC SOCIAL MEDIA”, by Li Lei et al., is totally incorporatedherein by reference.

The disclosure of co-pending and commonly assigned US Patent ApplicationPublication No. 2013/0086072, published Apr. 4, 2013, entitled, “METHODAND SYSTEM FOR EXTRACTING AND CLASSIFYING GEOLOCATION INFORMATIONUTILIZING ELECTRONIC SOCIAL MEDIA”, by Peng Wei et al., the content ofwhich is totally incorporated herein by reference.

BRIEF DESCRIPTION

A first embodiment of the disclosure relates to a method for respondingto a message posted in a social media stream. The method includesmonitoring a social media site for at least one message including selectsubject matter. In response to identifying a message, the methodincludes collecting a series of exchanges that form a conversationalthread including the message. The method includes determining at leastone content attribute of the message. The method includes classifyingthe message using at least one key attribute. The method includessearching a database for a reference message using a combination of theat least one content and key attributes. The method includes determininga previous outcome of a reference thread including the referencemessage. The method includes using the previous outcome for determininga course of action.

Another embodiment of the disclosure relates to a system for respondingto a message posted in a social media stream. The system includes anauto-suggesting response device including a processor in communicationwith the memory for executing modules. The auto-suggesting responsedevice includes a message monitoring module adapted to monitor a socialmedia site for at least one message including select subject matter. Inresponse to identifying a message, the message monitoring modulecollects a series of exchanges that form a conversational threadincluding the message. The auto-suggesting response device includes acategorization module adapted to categorize the message into one of apredetermined set of categories. The auto-suggesting response deviceincludes a response search module adapted to search a database for areference message using a combination of the at least one content andkey attributes. The response search module determines an outcome of areference thread including the reference message. The auto-suggestingresponse device includes a response generation module adapted to use theoutcome to form a response for providing in the thread.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an overview of a method 10 for generating a suggestedresponse.

FIG. 2 is a schematic illustration of a social CRM system for monitoringsocial media sites for issues and generating responses to the issuesaccording to one embodiment.

FIG. 3 shows a method 300 for generating suggested responses to aconversational thread posted in a social media site.

FIG. 4 shows a sample screenshot generated as output by the method ofFIG. 3.

DETAILED DESCRIPTION

The present disclosure relates to a system and method that automaticallysuggests responses based on social conversational content in customercare services. The system and method responds to issues expressed onsocial media platforms. The system uses contextual information,extracted from a conversational thread, to customize a query forretrieving application-specific configurations. Results of the query areused to automatically rank suggested responses.

FIG. 1 shows an overview of a method 10 for generating a suggestedresponse. The method starts at S12. The system identifies and leveragescontextual information associated with a particular conversationalthread on social media at S14. The contextual information is extractedfrom the thread at S16. This contextual information includes, forexample, slug/tags, topics, category and sequence information, feedbacksentiments, and user locations. By extracting relevant contextualinformation, the system filters out noise in the social conversation.The extracted information is used to query reference conversationsdiscussing a similar topic at S18. By querying ‘referenceconversations’, the system is searching for previously handled customerservice matters that resulted in positive customer feedback. Thecontextual information is used to retrieve possible responses and/orsolutions that are relevant to the topic of the thread at S20. Theextracted contextual information enables the system to retrieve morerelevant responses verses a broader approach that relies on keywordand/or topic matching. The system automatically generates a list of thepossible suggestions and/or responses at S22. In one embodiment, thesystem can rank the suggestions on the list at S24. The method ends atS26.

FIG. 2 is a schematic illustration of a social CRM system 100 in oneexemplary embodiment. The system 100 includes an automatic suggestedresponses device (hereinafter “the auto-suggesting response device”)device 102, hosted by a computing device, such as a client computerand/or server device, and a user device 104, similarly hosted by aclient computing device, which are linked together by communicationlinks 106, referred to herein as a network. These components aredescribed in greater detail below.

The auto-suggesting response device 102 illustrated in FIG. 2 includes acontroller or digital front end (“DFE”) 108 that is part of orassociated with the device 102. The exemplary controller 108 is adaptedfor controlling an analysis of message data 140 (i.e., conversationalthread) received by the system 100. The controller 108 includes aprocessor 110, which controls the overall operation of the device 102 byexecution of processing instructions that are stored in memory 112connected to the processor 110.

The memory 112 may represent any type of tangible computer readablemedium such as random access memory (RAM), read only memory (ROM),magnetic disk or tape, optical disk, flash memory, or holographicmemory. In one embodiment, the memory 112 comprises a combination ofrandom access memory and read only memory. The digital processor 110 canbe variously embodied, such as by a single-core processor, a dual-coreprocessor (or more generally by a multiple-core processor), a digitalprocessor and cooperating math coprocessor, a digital controller, or thelike. The digital processor, in addition to controlling the operation ofthe device 102, executes instructions stored in memory 112 forperforming the parts of the method outlined in FIGS. 1 and 3. In someembodiments, the processor 110 and memory 114 may be combined in asingle chip.

The device 102 may be embodied in a networked device, although it isalso contemplated that the device 102 may be located elsewhere on anetwork to which the system 100 is connected, such as on a centralserver, a networked computer, or the like, or distributed throughout thenetwork or otherwise accessible thereto. The attribute identificationand response search and classification phases disclosed herein areperformed by the processor 110 according to the instructions containedin the memory 112.

In particular, the memory 112 stores a message monitoring module 114,which monitors a social media site and collects conversational threadsabout a select topic; a tag and slug generation module 116, whichextracts tags from at least one message within the thread and generatesslugs from the tags; a categorization module 118, which categorizes themessage into one of a predetermined set of categories classifying apurpose of them message; a topic determination module 120, whichcompares the message to a stored dictionary to classify the message asbelonging to one of a set of predetermined topics; a locationdetermination module 122, which identifies a location of a deviceposting the message; a response search module 124, which searches adatabase for reference messages having similar attributes; a messageclassification module 126, which rates a confidence of each referencemessage retrieved from the database; and, response generation module128, which determines an outcome of a previous thread containing thereference message and provides the outcome as a suggested response tothe conversational thread. Embodiments are contemplated wherein theseinstructions can be stored in a single module or as multiple modulesembodied in the different devices. For example, the instructions forexecuting steps in modules 116-120 can be included in one attributedetermination module. The modules 114-128 will be later described withreference to the exemplary method.

The software modules 114-128 as used herein, are intended to encompassany collection or set of instructions executable by the device 102 orother digital system so as to configure the computer or other digitalsystem to perform the task that is the intent of the software. The term“software” as used herein is intended to encompass such instructionsstored in storage medium such as RAM, a hard disk, optical disk, or soforth, and is also intended to encompass so-called “firmware” that issoftware stored on a ROM or so forth. Such software may be organized invarious ways, and may include software components organized aslibraries, Internet-based programs stored on a remote server or soforth, source code, interpretive code, object code, directly executablecode, and so forth. It is contemplated that the software may invokesystem-level code or calls to other software residing on a server (notshown) or other location to perform certain functions. The variouscomponents of the auto-suggesting response device 102 may be allconnected by a bus 130.

With continued reference to FIG. 2, the device 102 also includes one ormore communication interfaces 132, such as network interfaces, forcommunicating with external devices. The communication interfaces 132may include, for example, a modem, a router, a cable, and/or Ethernetport, etc. The communication interfaces 132 are adapted to receive themessage data 140 as input.

The device 102 may include one or more special purpose or generalpurpose computing devices, such as a server computer or digital frontend (DFE), or any other computing device capable of executinginstructions for performing the exemplary method.

FIG. 2 further illustrates the auto-suggesting response device 102connected to user device 104, such as an expert handling agent's device,which can transmit an instruction for the auto-suggesting responsedevice to perform the process disclosed here, or for receiving thesuggested responses 138 generated by the auto-suggesting responsedevice.

In one embodiment, the user device 104 can be adapted to relay and/ortransmit the message data 140 to the auto-suggesting response device102. In another embodiment, the message data 140 may be input from anysuitable source, such as a workstation, a database, a memory storagedevice, such as a disk, or the like.

With continued reference to FIG. 2, the system 100 includes a storagedevice 134 that is part of or in communication with the auto-suggestingresponse device 102. In a contemplated embodiment, the device 102 can bein communication with a server (not shown) that includes a processingdevice and memory, such as storage device 134, or has access to astorage device 134, for storing the tag dictionary, referenceconversations, and/or historical data.

With continued reference to FIG. 2, the message data 140 undergoesprocessing by the auto-suggesting response device 102 to output thedesired information. In one embodiment, this information can includesuggested responses 138 for a user 142, such as an expert handlingagent, to review. IN another embodiment, this information can include alist of suggested responses, and/or a ranking between a number ofsuggested responses.

The suggested response information is provided to the user 142 in asuitable form on a graphic user interface (GUI) 136 or to a user device104, such as a computer belonging to the expert handling and/or customerservice agency. The GUI 136 can include a display, for displayinginformation to users, and a user input device, such as a keyboard ortouch or writable screen, for receiving instructions as input, and/or acursor control device, such as a mouse, trackball, or the like, forcommunicating user input information and command selections to theprocessor 110.

FIG. 3 shows a method 300 for generating suggested responses to aconversational thread posted in a social media site. Certain contextsimpact the effectiveness of an engagement between expert handling agentsand customers, and/or users, of the social media site. These contextsinclude, for example, topics of conversational threads and messagesforming a conversational thread, a sentiment expressed by a user postinga message in the thread, a location where the user is posting themessage, and a structure of the dialog. By “structure”, the disclosuremeans a category of the message and a sequence of messages, etc.Therefore, the present disclosure relates to a method that analyzes theforegoing contexts and uses the analysis to suggest responses forassisting the handling agent responding to the message. The method aimsto improve the effectiveness of the expert handling agent at resolvingthe issue and also the overall customer service experience of the user.The method starts at S302.

The system monitors a social media site for a message containing selectsubject matter at S304. Generally, the system is monitoring the socialmedia site for conversations related to, for example, opinions,grievances, complaints, questions, and/or feedback (hereinaftercollectively referred to as “the question” for simplifying a descriptionof one contemplated embodiment) about a product, service, brand, and/orproduct (hereinafter collectively referred to as “the select subjectmatter”). Through monitoring the social media site, the messagemonitoring module 114 identifies the question conveyed in at least onemessage contained in a conversational thread published on the socialmedia site. In response to identifying a message containing the selectsubject matter, the module 114 collects the conversational threadincluding the message at S306.

The disclosure relates to a process for finding responses/answers to thequestion from similar conversational threads stored in a historicalconversation database. As such, the process relies on certain input foroutputting the related responses. Accordingly, the system 100 determinesattributes at S308 that it associates as the input variables in alater-performed search.

The tag and slug determination module 116 determines a content attributeat S310. More specifically, the module 116 determines a first attributeincluding at least one of tags and slugs. The module 116 extracts tagsfrom the exchanges and/or messages forming the conversational thread atS312.

To extract tags, the module 116 splits the message (or, in certainembodiments, the entire conversational thread) into separate words. Thewords are parsed to generate stem-words to be used in a comparison withprevious reference messages in the historical database. The stem-wordsinclude normalized words, i.e., key terms, from the message. In oneembodiment, a text processing parser can parse the split words.

A filter can filter through the split words and remove undesired andirrelevant words form the message at S314. In this manner, the module116 can associate as tags the key terms extracted from the entiremessage and/or conversational thread.

The table 1 below illustrates how reference tags and slugs are stored ina database for previous postings on a social media site. Forillustrative purposes, the postings in the example include tweets thatwere posted on the Twitter media site. However, the method disclosedherein is amendable to other social media contents and conversations,such as Facebook wall posts, comments, status updates, and blog entries,etc.

TABLE 1 Original Tweet When you planned to release 4G mobiles in NYC?Split words When, you, planned, to, release, 4G, mobiles, in, NYC Parsedwords when, you, plan, to, release, 4g, mobile, in, nyc Removing

,

, plan,

, release, 4g, mobile,

, nyc unwanted words Wanted words or plan, release, 4g, mobile, nyc Tags

The table shows that the original message includes a question asking acompany when it planned to release an anticipated product. The module116 split the message into a number of words forming the entire message.The module 116 parsed the words to determine each stem. The module 112filtered and removed undesired stem-words. In the table, the examplestem-words include the product (4g and mobile) and the location (NY)where access to the product is desired. The module 116 associated theremaining stem-words as tags.

The module 116 can combine the tags to form a slug at S316. By “slug”,the disclosure refers to a combination of tags forming a generic termwhich can be used in a comparison with the reference messages andthreads previously handled and stored in the database. The tags or slugsare used as a first attribute in the later-discussed comparison.

Another attribute that the system determines is a category of eachmessage. By “category”, the disclosure means the purpose that messageset forth to convey, such as, inter alia, a question, a complaint, agreeting, an answer, and, a request, etc. A categorization module 118automatically detects the category of the message in the conversationalthread at S318. The module 118 categorizes the message into one of apredetermined set of categories for classifying the purpose of themessage at S320. In one embodiment, the module 118 can parse the contentin the message, and apply the parsed content as input in acategorization model. In one embodiment, the module 118 can determinethe category using the approach described in the disclosure ofco-pending and commonly assigned US Patent Application Publication No.2013/0080212, Published Mar. 28, 2013, entitled, “METHODS AND SYSTEMSFOR MEASURING ENGAGEMENT EFFECTIVENESS IN ELECTRONIC SOCIAL MEDIA”, byLi Lei et al., the content of which is totally incorporated herein byreference. In US Patent Publication No. 2013/0080212, an entireconversation history, being a thread of exchanges between a user and areplying agent on a particular topic, is stored in a database, measured,and analyzed.

Table 2 below illustrates example category values output as a result ofthe sample messages used as input. The model can output a categoryvalue. In one embodiment, the module 118 can detect where the messagebelongs within a sequence of messages forming the thread.

TABLE 2 Category Examples Question When balance will be credited to myaccount? Complaint My phone won't let me send texts even though my billisn't due for two days. Request Is there anything The Social Media Teamcan assist you with? Answer Yes please fix the issue ASAP AnnouncementThat's understandable, but please keep in mind that we will doeverything in our power to resolve any issue. Reception Wow that greatand good to hear Thanking Your feedback is greatly appreciated. ResponseYou are welcome! Greeting Hi Hello there Apologies We are really verysorry to hear that. Solved Finally I got my message pack last night.

The words that were parsed from the split message at S312 (or S320) canbe used to identify another attribute, being a topic of the message atS322. The words are compared with a stored dictionary at S324. Mainly,the dictionary associates predefined sets of words with particulartopics. The dictionary includes a list of words that are previouslybuilt and assigned to each topic and associated with a specific areawithin the topic. For example, the previously built dictionaryassociates the words “mobile, network, message, down, data” shown in theillustrated example in Table 1 as belonging to the topic “mobilenetwork”, and therefore the module 120 can classify the message asbelonging to the “mobile network” topic when the split words arecompared with the topics. The message is classified as belonging to theidentified topic at S326. In the illustrative embodiment, a supervisedlearning approach can be used to identify the topic.

In a scenario where the combinations of words generate a number ofmatches for different topics, the module 120 identifies the topicattribute as being the topic resulting from the most words in thecombination. In other words, if five split words are used in thecomparison, and a combination of three words produces a few matches fortopics, and a combination of four words produces a few more matches fortopics, but all five words result in a single match for a topic, thesystem associates the latter topic (corresponding to the five wordresult) as the topic attribute.

In a similar manner, a sentiment of the message can be identified. Adictionary or database can associate a number of words with one of anumber of predetermined sentiments. In one embodiment, the identifiedsentiment can be one of three predetermined (positive/neutral/negative)sentiments. In another embodiment, the module 120 can assign the wordsto a sentiment attribute belonging to one of five(positive/somewhat-positive/neutral/somewhat-negative/negative)categories.

In an alternate embodiment, crowd sourcing options, understood in theart, can be used to identify the topic and sentiment of the message.

Continuing with FIG. 3, another attribute that the system can determineat S308 is a location. A location determination module 122 determines alocation of the device posting the message about the select subjectmatter at S328. The module 122 can identify multiple locationsinformation associated with a single message. For social networkingsites that allow users to add location information in a user-profile orinside a message, the module 122 can identify the location using thecheck-in location information within the message and/or user-profile. Inanother embodiment, the location information may be specified within themessage itself, such as in the illustrated example set forth in theoriginal message of Table 1 asking a company about the release of acertain product. The module 122 can identify the location based on thewords extracted from the message.

The location information can be extracted and predicted based on a geolocation extraction technique, such as by using the approach describedin the disclosure of co-pending and commonly assigned US PatentApplication No. US Patent Application Publication No. 2013/0086072,published Apr. 4, 2013, entitled, “METHOD AND SYSTEM FOR EXTRACTING ANDCLASSIFYING GEOLOCATION INFORMATION UTILIZING ELECTRONIC SOCIAL MEDIA”,by Peng Wei et al., the content of which is totally incorporated hereinby reference. In US Patent Application Publication No. 2013/0086072, thelocation is identified based on a learned content-basedlocation-prediction model, which detects a user's geographic locationfrom the message content as being one of a number of predeterminedcategories, such as a previous, current, or and future location.

The location information is an important attribute drilling down thesearch results and for determining suggested responses that are relevantfor a particular geographical area. This is because the location, andall the attributes determined at S308 for a select, incoming message,are used in a comparison with attributes from reference messages.

Continuing with FIG. 3, the attributes are used to search the referencedatabase at S330. More specifically, a combination of the attributesassociated with the current message and thread are used as input in aquery for searching a database of reference messages and threads thathandled, to completion, the same or similar issues that form the basisof the select message. Using the determined attributes, the responsesearch module 122 uses different combinations of the contextualattributes (such as, tags, slug, sliced words, category, and sequencewithin the thread, topic, sentiment, and relevant user locationinformation) to search for matching messages in reference conversationalthreads. Various combinations of the attributes are matched againstreference threads stored in the historical database to determine whetherany previous messages are available with similar content.

Table 3 shows attribute values that were identified from the samplemessage illustrated in Table 1. For illustrative purposes, these valuesare matched against reference messages, i.e., previous messages havingrelated content, based on pre-configured criteria.

TABLE 3 Attributes Values Original Tweet When you planned to release 4Gmobiles in NYC? Tags plan, release, 4g, mobile, nyc Slugs4g_mobile_release, 4g_mobile_release_plan, mobile_release_plan,4g_mobile_release_plan_nyc Category Question Topic Mobile releaseLocation NYC

In one embodiment, a number of searches of the database are performedwith various combinations of attributes. However, the priority andsequence of these searches is based on the particular attributesselected for the combination. In a first search of the database, areference message matching the exact message using a combination of thecategory, topic, and location attributes at S332. The results of thatsearch are filtered for determining whether a reference message exactlymatches the content of the original message. Table 4 shows the attributevalues for the category, topic, and location attributes, as well as thematching content results for the illustrative example.

TABLE 4 Match query— Category Topic Location Exact tweet contentQuestion Mobile Release NYC When you planned to release 4G mobiles inNYC?

A second search of the database is performed for matching the slugs,determined for the original message (at S316), to a reference messageusing a combination of the category, topic, and location attributes atS334. The results of that search are filtered for determining whether areference message exactly matches the slugs of the original message.Table 5 shows the attribute values for the category, topic, and locationattributes, as well as the matching slug results for the illustrativeexample.

TABLE 5 Category Topic Location Match query—Slugs Question Mobile NYCmobile_release_plan, Release 4g_mobile_release, 4g_mobile_release_plan,4g_mobile_release_plan_nyc

A third search of the database is performed for matching the tags,determined for the original message (at S312), to a reference messageusing a combination of the category, topic, and location attributes atS336. The results of that search are filtered for determining whether areference message exactly matches the tags of the original message.Table 6 shows the attribute values for the category, topic, and locationattributes, as well as the matching tag results for the illustrativeexample.

TABLE 6 Category Topic Location Match query—Tags Question Mobile ReleaseUSA plan, release, 4g, mobile, nyc

As mentioned, the searches at S332-336 are performed based onpredetermined sequence, and each result of the individual searches canbe assigned a confidence score based on a priority of the searches atS338. Mainly, the message classification module 126 assigns scores forthe available responses made in reference messages based on thepercentage of match and level of match at S338.

In response to the search at S332 resulting in an exact match, the replyused to resolve and/or conclude a reference thread including thereference message can be used as a suggested response for the currentmessage. Because this level of “exact” matching does not occurfrequently, the module 126 treats the results as being the mostaccurate. At S340, the module 126 assigns a high accuracy score to thereference response made for the message determined at S332, thus placinga high confidence on using, as a suggested response, the referenceresponse generating the outcome in the reference message.

The module 126 considers the level of matching using slugs as beingquite accurate. In response to the search at S334 resulting in amatching reference message, the module 126 assigns a medium accuracyscore to the reference response made for the message determined at S344,thus placing medium-level confidence on using, as a suggested response,the reference response generating the outcome in the reference message.

The module 126 considers the level of matching using tags as being lessaccurate. In response to the search at S336 resulting in a matchingreference message, the module 126 assigns a low accuracy score to thereference response made for the message determined at S346, thus placinga low-level of confidence on using, as a suggested response, thereference response generating the outcome in the reference message.

Continuing with FIG. 3, the response generation module 128 collects thereference answers resulting from the search queries at S346. All of thepossible responses are obtained from the query comparisons and matches.From these responses, the module 128 accesses each reference thread,which included the select reference message and response, and determinesan outcome of the thread at S348. One aspect of the system 100 is that,by examining the outcome of the thread, it identifies which responsesresulted in positive feedback from the user/customer. In this manner,the expert handling agent, replying to the select message, can formulateits response based on a knowledge of previous outcomes. The handlingagent can focus suggested responses associated with positive feedbackand avoid the suggested responses associated with negative feedback.This approach enhances a quality of customer care service by increasingthe quality of responses available to the expert handling agent.

To review the outcome, the system analyzes the user-posted response,received after the reference handling agent's response was posted to thereference conversational thread. In one embodiment, this user-responsecan be the next posting in the reference thread. The module 128determines whether the outcome was received one of positive andnegatively by the user at S350. In response to the user expressing apositive sentiment (P at S350), the module 128 classifies the referenceresponse as belonging to a suggested positive response at S352. Inresponse to the user expressing a negative sentiment (N at S350), themodule 128 classifies the reference response as belonging to a negativeresponse at S354. In one embodiment, the reference responses classifiedas being negative are not included as suggested responses.

The suggested positive responses are displayed as references for thecustomer care handling expert/agent reference at S356. In the suggestedpositive responses can be ranked based on the accuracy scores assignedto the reference responses (at S338) at S358. The suggested list ofresponses can be provided to the user based on a hierarchy ofconfidence. In another embodiment, the system can display the matchingresponses based on pre-configured criteria, which articulates a weightedfactor for each contextual attribute when scoring the relevance of eachsuggested response. For example, the sample message illustrated forTable 1 sought a release date for a product in New York. In oneembodiment, the system can configure the “user location” attribute ashaving higher importance within the searching and ranking operations.

The suggested list of responses can be used for determining a course ofaction at S360. In this manner, the chances are increased forreplicating a previously successful outcome, thus enhancing the qualityof the customer service. By displaying the list of positive suggestedresponses without the negative suggested responses, the system 100further increases its confidence that a selected response will resolvethe issue, thus improving the quality of service.

The module 128 can further display a list of negative suggestedresponses for the handling expert to consider avoiding. Similarly, thesystem can rank these negative responses based on the confidence scoreprovided at S338. FIG. 4 shows a sample screenshot generated as outputby the method of FIG. 3, including a list of suggested positiveresponses, for considering, and a second list of suggested negativeresponses, for avoiding. The method ends at S362.

Although the method is illustrated and described above in the form of aseries of acts or events, it will be appreciated that the variousmethods or processes of the present disclosure are not limited by theillustrated ordering of such acts or events. In this regard, except asspecifically provided hereinafter, some acts or events may occur indifferent order and/or concurrently with other acts or events apart fromthose illustrated and described herein in accordance with thedisclosure. It is further noted that not all illustrated steps may berequired to implement a process or method in accordance with the presentdisclosure, and one or more such acts may be combined. The illustratedmethods and other methods of the disclosure may be implemented inhardware, software, or combinations thereof, in order to provide thecontrol functionality described herein, and may be employed in anysystem including but not limited to the above illustrated system 100,wherein the disclosure is not limited to the specific applications andembodiments illustrated and described herein.

For example, in one contemplated embodiment, the system 100 can storeand search a database including a list of predefined conversations builtfrom common questions and issues frequently raised by customers.

The present system and method improves the effectiveness of handlingagents engaged in a social CRM environment. The system and methodfurther lowers a cost of training handling experts.

One aspect of the present method and system is that it can helpenterprise marketing services and CRM services to instantly andeffectively interact with their customers in a social CRM environment.By referring to existing social conversations, the system canautomatically provide suggestions for answers to customer queries.

By exploring and leveraging contextual information associated with aconversational thread communicated online, the system retrieve and rankcontext-specific, relevant responses from a conversation database. Oneaspect of the disclosed database, which is based on preconfiguredtextual information and/or a ranking scheme, is that the system canautomatically output suggested responses and/or options to customerqueries. These suggestions can be provided to handling experts and/oragents in the social CRM.

By generating both positive and negative suggestions, the system canhelp the handling expert avoid posting an undesired, negative response.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomany other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.

What is claimed is:
 1. A method for responding to a message posted in asocial media stream, the method comprising: monitoring a social mediasite for at least one message including select subject matter; inresponse to identifying a message, collecting a series of exchanges thatform a conversational thread including the message; determining at leastone content attribute of the message; classifying the message using atleast one key attribute; searching a database for a reference messageusing a combination of the at least one content and key attributes;determining a previous outcome of a reference thread including thereference message; and, using the previous outcome for determining acourse of action.
 2. The method of claim 1, wherein the at least onecontent attribute is selected from a group consisting of: a set of tagsextracted from the message; at least one slug generated from at leastone combination of tags; and, quoted text of the message.
 3. The methodof claim 1, wherein the key attribute is selected from a groupconsisting of: a category; a topic that the message relates to; alocation; and, a combination of the above.
 4. The method of claim 1further comprising: rating a confidence of the reference message basedon the combination of the at least one content and key attributes usedin the searching.
 5. The method of claim 1 further comprising:extracting a set of tags forming the message; forming at least onecombination of tags to generate at least one slug; associating each slugas a content attribute; searching the database for the reference messageby comparing the each slug with reference messages.
 6. The method ofclaim 2 further comprising: extracting a set of tags forming themessage; filtering and removing irrelevant tags from the set of tags;associating the filtered tags as the content attribute; searching thedatabase for the reference message by comparing the filtered tags withreference messages.
 7. The method of claim 1, further comprising:categorizing the message into one of a predetermined set of categoriesclassifying a purpose of the message; and, searching the database usingat least the category one key attribute in the combination.
 8. Themethod of claim 1 further comprising: comparing tags in the message witha stored dictionary for identifying at least one topic of the message;classifying the message as belonging to the topic; and, searching thedatabase for the reference message using at least the topic as one keyattribute in the combination.
 9. The method of claim 1 furthercomprising: determining a location of a device posting the message; andsearching the database for the reference message using at least thelocation as one key attribute in the combination.
 10. The method ofclaim 1, wherein the determining the course of action includes:accessing a database of reference threads to determine if the messagecorresponds to one of a set of reference threads; for the message beingdetermined as corresponding to a reference thread, identifying asentiment of a last message in the reference thread as belonging to oneof a negative and a positive sentiment; for the message being determinedas not corresponding to a reference thread, providing the message to anadvisor for review.
 11. The method of claim 7, wherein the determiningthe course of action includes: for the message belonging to the positivesentiment, providing the previous outcome to the advisor as a suggestedresponse to the thread; and, for the message belonging to the negativesentiment, providing the message to the advisor for review.
 12. Themethod of claim 7, wherein the reference threads include previousthreads that were concluded.
 13. A computer program product comprisingtangible media which encodes instructions for performing the method ofclaim
 1. 14. A system for responding to a message posted in a socialmedia stream, the system comprising: an auto-suggesting response devicecomprising: a message monitoring module adapted to: monitor a socialmedia site for at least one message including select subject matter,and, in response to identifying a message, collect a series of exchangesthat form a conversational thread including the message, acategorization module adapted to categorize the message into one of apredetermined set of categories, a response search module adapted to:search a database for a reference message using a combination of the atleast one content and key attributes, determine an outcome of areference thread including the reference message, and a responsegeneration module adapted to use the outcome to form a response forproviding in the thread; and, a processor, in communication with thememory for executing the modules.
 15. The system of claim 12, furthercomprising a tag and slug generation module adapted to: extract a set ofthe tags forming the message; filter and remove irrelevant tags from theset of tags; and, wherein the response search module is further adaptedto search the database for the reference message by comparing thefiltered tags with reference messages.
 16. The system of claim 12,wherein the predetermined set of categories includes at least one of:reasons why messages can be posted in the thread; types of sentimentsthat the message conveys based on tags in the message; topics ofmessages; locations of messages; and, a combination of the above. 17.The system of claim 12, further comprising a topic identification moduleadapted to: compare tags in the message with a stored dictionary foridentifying at least one topic of the message; assign the topic to themessage; and, wherein the response search module is further adapted to:search the database for the reference message using the topic.
 18. Thesystem of claim 12, further comprising a location identification moduleadapted to determine a location of a device posting the message, whereinthe response search module is further adapted to search the database forthe reference message using the location.
 19. The system of claim 12,further comprising a tag and slug determination module adapted to: formcombinations of tags to generate slugs; wherein the categorizationmodule is further adapted to categorize the message into one of thepredetermined set of categories based on the slugs; and, wherein theresponse search module is further adapted to search the database for thereference message by comparing the slugs with reference messages. 20.The system of claim 12, further comprising a message classificationmodule is further adapted to: in response to the response search moduleaccessing a database of reference threads to determine if the messagecorresponds to one of a set of reference threads, for the message beingdetermined as corresponding to a reference thread, identify a sentimentof a last message in the reference thread as belonging to one of anegative and a positive sentiment.
 21. The system of claim 18, whereinthe response generation module is further adapted to: for the messagebelonging to the positive sentiment, providing as a reply to the messagefrom the reference thread; and, for the message belonging to thenegative sentiment, providing the message to the advisor for review. 22.The system of claim 19, wherein the reference threads include previousthreads that were concluded.